Upload app.py

app.py

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+# -*- coding: utf-8 -*-
+"""Untitled1.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Oyv-OC4NLyS4SOfffFLwdI0wqvX7K_q2
+"""
+
+!pip install transformers PyPDF2 gradio
+
+import gradio as gr
+from transformers import pipeline
+from PyPDF2 import PdfReader
+from huggingface_hub import InferenceClient
+from google.colab import userdata
+import requests
+from PIL import Image
+import io
+
+pipe = pipeline("text2text-generation", model="asach/simpleT5-resume-summarization")
+
+reader = PdfReader("/KennethGuillont.pdf")
+text = ""
+for page in reader.pages:
+  text += page.extract_text()
+
+summary = pipe(text, max_length=150, min_length=30)[0]['generated_text']
+summary
+
+my_key = userdata.get('HF')
+
+client = InferenceClient(api_key=my_key)
+
+model_name= 'meta-llama/Llama-3.2-3B-Instruct'
+
+agent_desc = """
+You are an AI agent helps a user generate a prompt to feed into an AI image
+generation model based on a summary of their resume given to you. The image should depict a rabbit
+within the the career feild related to the summary. encase the image prompt between
+two '---\n' marks, to separate it from the rest of the text.
+"""
+
+print(summary)
+
+messages = [
+    {"role": "user", "content": agent_desc},
+    {"role": "user", "content": summary}
+]
+
+stream = client.chat.completions.create(
+    model=model_name,
+    messages=messages,
+    max_tokens=700,
+    stream=True
+)
+
+response_text =""
+
+for chunk in stream:
+  response_text += chunk.choices[0].delta.content
+
+print(response_text)
+
+print(response_text.replace('.','.\n'))
+
+image_prompt = response_text.split('---\n')[1]
+image_prompt
+
+API_URL = "https://api-inference.huggingface.co/models/CompVis/stable-diffusion-v1-4"
+headers = {"Authorization": f"Bearer {my_key}"}
+
+def query(payload):
+	response = requests.post(API_URL, headers=headers, json=payload)
+	return response.content
+
+image_bytes = query({
+	"inputs": image_prompt,
+})
+# You can access the image with PIL.Image for example
+import io
+from PIL import Image
+image = Image.open(io.BytesIO(image_bytes))
+
+image